Network reliability‐based optimal toll design

Optimal toll design from a network reliability point of view is addressed in this paper. Improving network reliability is proposed as a policy objective of road pricing. A reliability‐based optimal toll design model, where on the upper level network performance including travel time reliability is optimized, while on the lower level a dynamic user‐equilibrium is achieved, is presented. Road authorities aim to optimize network travel time reliability by setting tolls in a network design problem. Travelers are influenced by these tolls and make route and trip decisions by considering travel times and tolls. Network performance reliability is analyzed for a degradable network with elastic and fluctuated travel demand, which integrates reliability and uncertainty, dynamic network equilibrium models, and Monte Carlo methods. The proposed model is applied to a small hypothesized network for which optimal tolls are derived. The network travel time reliability is indeed improved after implementing optimal tolling system. Trips may have a somewhat higher, but more reliable, travel time.     

Generation of synthetic datasets for discrete choice analysis

Despite the widespread use of synthetic data in discrete choice analysis, little is known about how the methodology used to generate synthetic datasets influences the properties of parameter estimates and the validity of results based on these estimates. That is, there are two potential sources of biases when using synthetic discrete choice data: (1) bias due to the method used to generate the dataset; and, (2) bias due to parameter estimation. The primary objective of this study is to examine bias due to the underlying data generation method. This study compares three methods for generating synthetic datasets and uses design of experiments and analysis of variance methods to investigate the ability to recover estimates for “true” logsum parameters for nested logit models. The method that uses nested logit probabilities to generate the chosen alternative results in unbiased parameter estimates. The method that is based on Gumbel error component approximations reveals that while the error components themselves are unbiased, subtle empirical identification problems can arise when these error components are combined with synthetically generated utility functions. The method that is based on normal error component approximations reveals that all logsum coefficients are biased upwards; the bias dramatically increases for those nests that have a low choice frequency and is most pronounced for those nests with high correlations among alternatives. Based on the results of the analysis, several recommendations for the generation of synthetic datasets for discrete choice analyses are provided.     

A joint flexible econometric model system of household residential location and vehicle fleet composition/usage choices

Modeling the interaction between the built environment and travel behavior is of much interest to transportation planning professionals due to the desire to curb vehicular travel demand through modifications to built environment attributes. However, such models need to take into account self-selection effects in residential location choice, wherein households choose to reside in neighborhoods and built environments that are conducive to their lifestyle preferences and attitudes. This phenomenon, well-recognized in the literature, calls for the specification and estimation of joint models of multi-dimensional land use and travel choice processes. However, the estimation of such model systems that explicitly account for the presence of unobserved factors that jointly impact multiple choice dimensions is extremely complex and computationally intensive. This paper presents a joint GEV-based logit regression model of residential location choice, vehicle count by type choice, and vehicle usage (vehicle miles of travel) using a copula-based framework that facilitates the estimation of joint equations systems with error dependence structures within a simple and flexible closed-form analytic framework. The model system is estimated on a sample derived from the 2000 San Francisco Bay Area Household Travel Survey. Estimation results show that there is significant dependency among the choice dimensions and that self-selection effects cannot be ignored when modeling land use-travel behavior interactions.     

Scrutinizing individuals’ leisure-shopping travel decisions to appraise activity-based models of travel demand

Activity-based models for modeling individuals’ travel demand have come to a new era in addressing individuals’ and households’ travel behavior on a disaggregate level. Quantitative data are mainly used in this domain to enable a realistic representation of individual choices and a true assessment of the impact of different Travel Demand Management measures. However, qualitative approaches in data collection are believed to be able to capture aspects of individuals’ travel behavior that cannot be obtained using quantitative studies, such as detailed decision making process information. Therefore, qualitative methods may deepen the insight into human’s travel behavior from an agent-based perspective. This paper reports on the application of a qualitative semi-structured interview method, namely the Causal Network Elicitation Technique (CNET), for eliciting individuals’ thoughts regarding fun-shopping related travel decisions, i.e. timing, shopping location and transport mode choices. The CNET protocol encourages participants to think aloud about their considerations when making decisions. These different elicited aspects are linked with causal relationships and thus, individuals’ mental representations of the task at hand are recorded. This protocol is tested in the city centre of Hasselt in Belgium, using 26 young adults as respondents. Response data are used to apply the Association Rules, a fairly common technique in machine learning. Results highlight different interrelated contexts, instruments and values considered when planning a trip. These findings can give feedback to current AB models to raise their behavioral realism and to improve modeling accuracy.     

Catching a ride on the information super-highway: toward an understanding of internet-based carpool formation and use

Fluctuating fuel prices, rising congestion, longer commutes, and related environmental and human health effects have combined to once more draw the interest of governments, commuters, and firms toward the concept of travel demand management (TDM). While TDM is not new, the proliferation of mobile telephony, fixed Internet, and associated applications has created fresh prospects for the implementation of commuter focused TDM strategies. One recent example is Carpool Zone, an on-line carpool-matching tool deployed and managed by the TDM group at Metrolinx, the regional transportation planning agency within Canada’s largest metropolitan region, the Greater Toronto and Hamilton Area. Using data provided by Metrolinx, this paper broadens current thinking on carpool formation and use. The main hypothesis guiding this work is that the carpool formation and use process is sensitive to personal and household characteristics, space, time, travel cost, and workplace TDM policies. Results from a logistic regression analysis suggest that geographical proximity to other users; workplace TDM policies; the scheduling of work; and commuter role preference increase the odds of successfully carpooling. Importantly, findings regarding the positive influence of workplace TDM policies suggest that Internet based TDM tools will likely require critically important investment in human capital at the back-end to ensure program participation.     

Applying social marginal cost pricing in rail PPPs: Present state, drawbacks and ways forward

The application of social marginal cost pricing (SMCP) in PPP's in the railway sector faces several challenges. We examine in detail the practical applicability of SMCP in railway PPPs from the perspectives of cost accounting and effectiveness of SMCP towards the allocative efficiency goal, addressing the likely drawbacks in conciliating the welfare objectives of SMCP with the objectives of project financing (cost recovery) and value for money that justify the realization of PPP's. To this end, we combine theoretical analysis with the observation of empirical results of a case study. We split the analysis per type of private service provision, which can be for service operation or infrastructure management. For infrastructure management, we recommend splitting the operator remuneration and the track access charges. For service operation, we argue that the correct decision on source of funding of the service operator should depend on the characteristics of the contract.     

STUDY ON MAXIMUM SPECIFIC SLUDGE ACIVITY OF DIFFERENT ANAEROBIC GRANULAR SLUDGE BY BATCH TESTS

The maximum specific sludge activity of granular sludge from large-scale UASB, IC and Biobed anaerobic reactors were investigated by batch tests. The limitation factors related to maximum specific sludge activity (diffusion, substrate sort, substrate concentration and granular size) were studied. The general principle and procedure for the precise measurement of maximum specific sludge activity were suggested. The potential capacity of loading rate of the IC and Biobed anaerobic reactors were analyzed and compared by use of the batch tests results.     

高尔夫A4型轿车行李箱盖的三维有限元计算与分析

在对高尔夫A4型轿车行李箱盖的使用材料性能进行实验研究的基础上,建立了三维数学模型,利用I-deas工程软件对其有限元计算,计算与实验结果相吻合.并通过对不同应用材料的有限元计算结果的分析,为生产厂家合理选材和正确使用材料提供了较准确的计算前提.     

A GIS-based planning approach to locating urban rail terminals

This paper develops a flexible GIS-based methodology for evaluating the potential locations of terminal park-and-ride facilities along urban rail lines. The methodology differs from political-based approaches and traditional travel demand modeling in its use of an objective measure of accessibility to gage the suitability of a site. The methodology begins by constructing an index of derived demand for rail usage based on the local demographics. Principal component analysis (PCA) is used as a means of constructing an index of derived demand consistent with other passenger surveys. Next, trade areas or commutersheds are calculated for each candidate park-and-ride location based on realistic measures of accessibility and network based drive times, taking into account competition among candidates for riders. Following the analysis, the candidate locations and their commutersheds are delineated and visualized in the GIS environment. In summary, application of this method produces a site-specific suitability index that may be used to rank and compare potential park-and-ride locations. We illustrate how our approach fits within the context of the larger-scale corridor study as a complimentary means of refining the location of urban rail stations. The analysis uses the proposed rail system for Columbus Ohio as a case study. This revised version was published online in June 2006 with corrections to the Cover Date.     

Measurements of Emissions to Air from a Marine Engine Fueled by Methanol

Emissions of exhaust gases and particulate matter from a dual fuel marine engine using methanol as fuel with marine gasoil as pilot fuel have been examined for ...